Haytham ElFadeel presented on the limitations of traditional database systems and introduced key-value storage systems as a next generation storage solution. Traditional databases are complex, have poor performance, and do not scale well. In contrast, key-value storage systems have a simple data model of key-value pairs, are designed from the start to scale horizontally across many machines, and can provide much better performance. Examples of popular key-value storage systems discussed included Amazon Dynamo, Facebook Cassandra, Redis, and MongoDB.

1. Haytham ElFadeel
Researcher in Computer Sciences
Level 300

2. Agenda
• Introduction
– Glance at the Scalable systems.
– What the available storage solution.
– The problem with the current solutions.
– The problem with the Database
• The Next-Generation of Storage System
– Key-Value store systems.
– Performance comparison.
• How it’s works
• Discussions, Q/A

3. Glance at Scalable Systems
• Scalable systems
– Scalability is the ability to provide better
performance when you add more computing
power.
– This performance gained should be relevant to the
added computing power.
– Examples: Google, Yahoo, Facebook, Amazon, eBay,
Orkut, Google App Engine, etc.

4. Glance at Scalable Systems
• Scalable types
– Vertical Scalability: Adding resource within the
same logical unit to increase the capacity. For
example: Add more CPUs, or expanding the storage
or the memory.
– Horizontal Scalability: Add multiple logical units of
resources and make them together work as a single
unit. You can think about it like: Clustering,
Distributed, and Load-Balancing.

5. Vertical Scalability vs. Horizontal
Scalability
Vertical Scaling
Limited Not limited
Software and HardwareHardware only
Horizontal Scaling

6. Vertical Scalability vs. Horizontal
Scalability
Haytham ElFadeel Quote:
If you need scalability, urgently, going to
vertical scaling is probably will to be the
easiest, but be sure that Vertical scaling, gets
more and more expensive as you grow, and
While infinite horizontal linear scalability is
difficult to achieve, infinite vertical scalability
is impossible.

7. Vertical Scalability vs. Horizontal
Scalability
Haytham ElFadeel Quote:
On the other hand Horizontal scalability
doesn’t require you to buy more and more
expensive hardware. It’s meant to be scaled
using commodity storage and server solutions.
But Horizontal scalability isn’t cheap either.
The application has to be built ground up to
run on multiple servers as a single application.

8. Glance at Scalable Systems
• Facebook
– More than 200,000,000 active user.
– 50,000 photo uploaded per minute.
– The most active social-network in the Web.
• Facebook chat
– The main challenge is maintain the users status.
– Distribute the load should depend on the users,
and they friends to avoid the traveling.
– Building a system that should scale from that start
to serve 100,000,000 user is really hard.

9. Glance at Scalable Systems
• Amazon
– More than 10,000,000 transition in every holidays.
– The Reliability of the user shopping cart is not
option.
• Google, Yahoo, Microsoft, Kngine, etc
– Processing huge amount of data, more than 1TB.
– Sorting the index by the rank value. Which means,
sort more than 1TB of data.
– Save the Crawled Web pages.

10. The Available Storage Solutions
• Memory:
– Just a Data Structure :)
• Disk:
– Text File: { XML, Protocol Buffer, Json }
– Binary File: { Serialized, custom format }
– Database: { MySQL, SQL Server, SQLLite, Oracle }

11. The Available Storage Solutions
• Memory:
– Just a Data Structure :)
• Disk:
– Text File: { XML, Protocol Buffer, Json }
– Binary File: { Serialized, custom format }
– Database: { MySQL, SQL Server, SQLLite, Oracle }
Bad performance
Not portable, questions
about performance
Bad performance,
Complex, huge latency.
What about capacity

12. The Problem with the Database
• Causes
– Old and Very complex system.
– Many wasted features.
– Many steps to process the SQL query.
– Need administration, and others.

13. The Problem with the Database
• Causes
– Old and Very complex system.
• The RDMS is very complex system, just like Operating
System:
– Thread Scheduling, Deadlock monitor, Resource manager.
– I/O Manager, Pages Manager, Execution Plan Manager.
– Case Manager, Memory Manager, Transaction Manager, etc.
• Most of DBMS architecture, designs, algorithms came up
around 1970s:
– Different hardware, platform properties.
– Old architecture, design, and algorithms.
Please review resource #1

14. The Problem with the Database
• Causes
– Many wasted features.
• Today systems have very rich features, simply because
they think that ‘one size fits all’:
– CLR Types, CLR Integration, Replication, Functions.
– Policy, Relations, Transaction, Stored procedure, ACID, etc.
• You can even call a Web Service from SQL Server! All this
mess, make the database appear like a platform and
development environment.

15. The problem with the Database
• Causes
– Many Steps to process the query.
• Parse the Query.
• Build the expression tree, and resolve the relational
algebra expression.
• Optimize the expression tree.
• Choice the execution plan.
• Start execute.
Please review resource #2, #3

16. The problem with the Database
• Effects
– Bad Performance: Throughput, Resource usage,
Latency.
– Not Scalable.

17. The problem with the Database
• Effects
– Bad Performance: Throughput, Resource usage,
Latency:
• Even the faster DBMS ‘MySQL’ can’t provide more than
5,000 query per second*.
• Add to this the consumed resource, and the big latency.
* Depend on the configuration

18. The problem with the Database
• Effects
– Not Scale:
• The Database is not designed to scale.
• Even if you get a new PC and partition the Database you
will never get (accepted) good performance
improvement.
Please review resource #1

19. The problem with the Database
The Database give us ACID:
• Atomicity: A transaction is all or nothing.
• Consistency: Only valid data is written to the
database.
• Isolation: pretend all transactions are
happening serially and the data is correct.
• Durability: What you write is what you get.

20. The problem with the Database
The problem with ACID is that it gives you too
much, it trips you up when you are trying to
scale a system across multiple nodes.
Down time is unacceptable. So your system
needs to be reliable. Reliability requires
multiple nodes to handle machine failures.
To make a scalable systems that can handle lots
and lots of reads and writes you need many
more nodes.

21. The problem with the Database
Once you try to scale ACID across many
machines you hit problems with network
failures and delays. The algorithms don't work
in a distributed environment at any acceptable
speed.
It’s a dead end

22. The Next generation of Storage
Systems
From long time ago many researches teams
and companies discovered that the database is
main bottleneck.
Many wasted features, bad performance, and
not designed for scale systems.

23. The Next generation of Storage
Systems
Building large systems on top of a traditional
RDBMS data storage layer is no longer good
enough.
This talk explores the landscape of new
technologies available today to augment your
data layer to improve performance and
reliability.
Please review resource #4

24. Key-Value Storage Systems
• Simple data-model, just key-value pairs.
• Every Value Assigned to Key.
• No complex stuff, such as: Relations, ACID, or
SQL quires.
• Simple interface:
– Get(key)
– Put(key, value)
– Delete(key) < Optional

25. Key-Value Storage Systems
• Designed from the start to scale to hundreds of
machines.
• Designed to be reliable, even if 50% of the
machines crashed.
• No extra work require to add new machine,
just plug the machine and it will work in
harmony.
• Many open source projects (C++, Java, Lisp).

26. Key-Value Storage Systems
• Who use such systems:
– Facebook.
– Google Orkut, Analysis.
– Google Web Crawling.
– Amazon.
– Powerset.
– eBay.
– Kngine.
– Yahoo.
– General using.
– Storing, and huge data analysis.
– Transactions, and huge data analysis.

27. Key-Value Storage Systems
You may wonder, can we really live without
Relations, ACID ?!
– The short answer: Absolutely Yes.
– The long answer: Absolutely Yes, But nothing for
free.

28. Key-Value Storage Systems
Now
You should make your decide
Take the blue pill
And see the truth
Or, Take the red pill
And stay in
wonderland

29. Key-Value Storage Systems
Key-Value Storage System, and other systems
built around CAP concept:
Consistency: your data is correct all the time.
What you write is what you read.
Availability: you can read and write and write
your data all the time.
Partition Tolerance: if one or more nodes fails
the system still works and becomes consistent
when the system comes on-line.

30. Key-Value Storage Systems
One Node - Performance Comparison (Web)
• MySql
– 3,030 sets/second.
– 4,670 gets/second.
• Redis
– 11,200 sets/second. (3.7x MySQL)
– 9,840 gets/second. (2.1x MySQL)
• Tokyo Tyrant
– 9,030 sets/second. (3.0x MySQL)
– 9,250 gets/second. (2.0x MySQL)
Please review resource #5

31. Key-Value Storage Systems
Two High-End Nodes - Performance Comparison
(Web)
• Redis
– 89,230 sets/second.
– 85,840 gets/second.

32. Key-Value Storage Systems
One Node - Performance Comparison
• SQL Server
– 2,900 sets/second.
– 3,500 gets/second.
• Vina*
– 10,100 sets/second. (3.4x SQL Server)
– 9,970 gets/second. (2.8x SQL Server)
* Vina : Key-Value Storage System used inside Kngine.

33. How it’s Works
Any Key-Value storage system, consist of two
primary layers:
– Aggregation Layer
– Storing Layer

34. How it’s Works
Any Key-Value storage system, consist of two
primary layers:
– Aggregation Layer
• Manage the instances, replication and distribution.
– Storing Layer
• One or many Disk-based Hash-Table.

35. How it’s Works (Storing Layer)
On the board

36. How it’s Works (Aggregation Layer)
• Received the requests.
• Route it to the target node.
• Manage Partitioning, and Replicas.
• The Partitioning, Replication done by
Consistence Hashing algorithm.
On the board
Please review resource #6

37. Key-Value Storage Systems
• Amazon Dynamo. < Paper
• Facebook Cassandra. < Open source
• Tokyo Cabinet/Tyrant. < Open source
• Redis < Open source
• MongoDB < Open source

38. Q / A

39. References
1. The End of an Architectural Era (It’s Time for a Complete
Rewrite). Paper.
2. Database Systems - Paul Beynon-Davies. Book.
3. Inside SQL Server engine - MS Press. Book.
4. Drop ACID and Think About Data. Highscalability.com.
5. Redis vs MySQL vs Tokyo Tyrant. Colin Howe’s Blog.
6. Consistent Hashing and Random Trees: Distributed
Caching Protocols for Relieving Hot Spots on the World
Wide Web. Paper.
7. Dynamo: Amazon’s Highly Available Key-value Store.
Paper.
8. Redis, Tokyo Tyrant project.
9. Consistent Hashing. Tom white Blog.

40. Resources
1. High Scalability blog.
Highscalability.com
1. It’s all about innovation blog.
Hfadeel.com/blog.
2. All Things Distributed.
Allthingsdistributed.com
3. Tom White blog
lexemetech.com

41. Thanks…
Dear all,
All of my presentation content it's open source.
Please feel free to use, copy, and re-distribute it.